# count items on columndomains_list = df['domains'].value_counts()# return first n rows in descending ordertop_domains = domains_list.nlargest(20)top_domains
Lista del top 20 de hashtags más usados y su frecuencia
Code
# convert dataframe column to listhashtags = df['hashtags'].to_list()# remove nan items from listhashtags = [x for x in hashtags ifnot pd.isna(x)]# split items into a list based on a delimiterhashtags = [x.split('|') for x in hashtags]# flatten list of listshashtags = [item for sublist in hashtags for item in sublist]# count items on listhashtags_count = pd.Series(hashtags).value_counts()# return first n rows in descending ordertop_hashtags = hashtags_count.nlargest(20)top_hashtags
# filter column from dataframeusers = df['mentioned_names'].to_list()# remove nan items from listusers = [x for x in users ifnot pd.isna(x)]# split items into a list based on a delimiterusers = [x.split('|') for x in users]# flatten list of listsusers = [item for sublist in users for item in sublist]# count items on listusers_count = pd.Series(users).value_counts()# return first n rows in descending ordertop_users = users_count.nlargest(20)top_users
# plot the data using plotlyfig = px.line(df, x='date', y='like_count', title='Likes over Time', template='plotly_white', hover_data=['text'])# show the plotfig.show()
Tokens
Lista del top 20 de los tokens más comunes y su frecuencia
Code
# load the spacy model for Spanishnlp = spacy.load("es_core_news_sm")# load stop words for SpanishSTOP_WORDS = nlp.Defaults.stop_words# Function to filter stop wordsdef filter_stopwords(text):# lower text doc = nlp(text.lower())# filter tokens tokens = [token.text for token in doc ifnot token.is_stop and token.text notin STOP_WORDS and token.is_alpha]return' '.join(tokens)# apply function to dataframe columndf['text_pre'] = df['text'].apply(filter_stopwords)# count items on columntoken_counts = df["text_pre"].str.split(expand=True).stack().value_counts()[:20]token_counts
q 3678
farc 2630
colombia 2396
paz 2222
d 2188
gobierno 1317
país 1240
santos 1104
gracias 850
petro 841
justicia 816
venezuela 784
uribe 770
bogotá 759
libertad 716
víctimas 708
años 708
columna 704
presidente 687
colombianos 633
Name: count, dtype: int64
Hora
Lista de las 10 horas con más cantidad de tweets publicados
Code
# extract hour from datetime columndf['hour'] = df['date'].dt.strftime('%H')# count items on columnhours_count = df['hour'].value_counts()# return first n rows in descending ordertop_hours = hours_count.nlargest(10)top_hours
Plataformas desde las que se publicaron contenidos y su frecuencia
Code
df['source_name'].value_counts()
source_name
Twitter for iPhone 14186
Twitter for BlackBerry® 8291
Twitter for Android 5049
Twitter Web Client 2627
Twitter for BlackBerry 841
TweetDeck 396
Twitter for iPad 239
Twitter for Android 207
Instagram 167
Twitter Web App 94
Periscope 84
Jetpack.com 75
Twitter for Android Tablets 73
Twitter Media Studio 73
Twitter for Websites 19
Twitter for Windows Phone 18
iOS 10
Twitlonger 4
erased5423693 4
Mobile Web (M2) 3
Twiffo 1
Name: count, dtype: int64
Tópicos
Técnica de modelado de tópicos con transformers y TF-IDF
Code
# visualize topicstopic_model.visualize_topics()
Reducción de tópicos
Mapa con 10 tópicos del contenido de los tweets
Code
# visualize topicstopic_model.visualize_topics()
Términos por tópico
Code
topic_model.visualize_barchart(top_n_topics=11)
Análisis de tópicos
Selección de tópicos que tocan temas de género
Code
# # selection of topics# topics = [0, 2, 3]# keywords_list = []# for topic_ in topics:# topic = topic_model.get_topic(topic_)# keywords = [x[0] for x in topic]# keywords_list.append(keywords)# # flatten list of lists# word_list = [item for sublist in keywords_list for item in sublist]# # use apply method with lambda function to filter rows# filtered_df = df[df['text_pre'].apply(lambda x: any(word in x for word in word_list))]# percentage = round(100 * len(filtered_df) / len(df), 2# print(f"Del total de {len(df)} tweets de @MariaFdaCabal, alrededor de {len(filtered_df)} hablan sobre temas de género, es decir, cerca del {percentage}%")
Code
# # drop rows with 0 values in two columns# filtered_df = filtered_df[(filtered_df.like_count != 0) & (filtered_df.retweet_count != 0)]# # add a new column with the sum of two columns# filtered_df['impressions'] = (filtered_df['like_count'] + filtered_df['retweet_count'])/2# # extract year from datetime column# filtered_df['year'] = filtered_df['date'].dt.year# # remove urls, mentions, hashtags and numbers# p.set_options(p.OPT.URL)# filtered_df['tweet_text'] = filtered_df['text'].apply(lambda x: p.clean(x))# # Create scatter plot# fig = px.scatter(filtered_df, x='like_count', # y='retweet_count',# size='impressions', # color='year',# hover_name='tweet_text')# # Update title and axis labels# fig.update_layout(# title='Tweets talking about gender with most Likes and Retweets',# xaxis_title='Number of Likes',# yaxis_title='Number of Retweets'# )# fig.show()